Brittany M Bogle1, Andrew W Asimos2, Wayne D Rosamond2. 1. From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill (B.M.B., W.D.R.); and Department of Emergency Medicine, Carolinas Healthcare System, Charlotte, NC (A.W.A.). bbogle@email.unc.edu. 2. From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill (B.M.B., W.D.R.); and Department of Emergency Medicine, Carolinas Healthcare System, Charlotte, NC (A.W.A.).
Abstract
BACKGROUND AND PURPOSE: The Severity-Based Stroke Triage Algorithm for Emergency Medical Services endorses routing patients with suspected large vessel occlusion acute ischemic strokes directly to endovascular stroke centers (ESCs). We sought to evaluate different specifications of this algorithm within a region. METHODS: We developed a discrete event simulation environment to model patients with suspected stroke transported according to algorithm specifications, which varied by stroke severity screen and permissible additional transport time for routing patients to ESCs. We simulated King County, Washington, and Mecklenburg County, North Carolina, distributing patients geographically into census tracts. Transport time to the nearest hospital and ESC was estimated using traffic-based travel times. We assessed undertriage, overtriage, transport time, and the number-needed-to-route, defined as the number of patients enduring additional transport to route one large vessel occlusion patient to an ESC. RESULTS: Undertriage was higher and overtriage was lower in King County compared with Mecklenburg County for each specification. Overtriage variation was primarily driven by screen (eg, 13%-55% in Mecklenburg County and 10%-40% in King County). Transportation time specifications beyond 20 minutes increased overtriage and decreased undertriage in King County but not Mecklenburg County. A low- versus high-specificity screen routed 3.7× more patients to ESCs. Emergency medical services spent nearly twice the time routing patients to ESCs in King County compared with Mecklenburg County. CONCLUSIONS: Our results demonstrate how discrete event simulation can facilitate informed decision making to optimize emergency medical services stroke severity-based triage algorithms. This is the first step toward developing a mature simulation to predict patient outcomes.
BACKGROUND AND PURPOSE: The Severity-Based Stroke Triage Algorithm for Emergency Medical Services endorses routing patients with suspected large vessel occlusion acute ischemic strokes directly to endovascular stroke centers (ESCs). We sought to evaluate different specifications of this algorithm within a region. METHODS: We developed a discrete event simulation environment to model patients with suspected stroke transported according to algorithm specifications, which varied by stroke severity screen and permissible additional transport time for routing patients to ESCs. We simulated King County, Washington, and Mecklenburg County, North Carolina, distributing patients geographically into census tracts. Transport time to the nearest hospital and ESC was estimated using traffic-based travel times. We assessed undertriage, overtriage, transport time, and the number-needed-to-route, defined as the number of patients enduring additional transport to route one large vessel occlusion patient to an ESC. RESULTS: Undertriage was higher and overtriage was lower in King County compared with Mecklenburg County for each specification. Overtriage variation was primarily driven by screen (eg, 13%-55% in Mecklenburg County and 10%-40% in King County). Transportation time specifications beyond 20 minutes increased overtriage and decreased undertriage in King County but not Mecklenburg County. A low- versus high-specificity screen routed 3.7× more patients to ESCs. Emergency medical services spent nearly twice the time routing patients to ESCs in King County compared with Mecklenburg County. CONCLUSIONS: Our results demonstrate how discrete event simulation can facilitate informed decision making to optimize emergency medical services stroke severity-based triage algorithms. This is the first step toward developing a mature simulation to predict patient outcomes.
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